Different portions of cardiac electrophysiological signals represent activities and functions of different cardiac tissue and circulation systems. Usually, surface ECG signal analyses based on electrophysiological activity (such as ECG signals and intra-cardiac electrograms) and time domain parameters of waveforms are utilized for cardiac arrhythmia detection and diagnosis, such as P wave distortion for detection of atrial fibrillation (AF) and ST segment changes for myocardial ischemia and infarction. However, known systems for cardiac arrhythmia identification and analysis based on ECG signals are subjective and need extensive expertise and clinical experience for accurate interpretation and appropriate cardiac rhythm management. Early arrhythmia recognition and characterization of myocardial ischemia and infarction, for example, is desirable for rhythm management of cardiac disorders and irregularities. Known systems analyze waveform morphologies and time domain parameters associated with cardiac depolarization and repolarization, such as P wave, QRS complex, ST segment, T wave, for cardiac arrhythmia monitoring and identification. Some known systems apply sophisticated mathematical theories to biomedical signal interpretation, such as for frequency analysis, symbolic complexity analysis and nonlinear entropy evaluation, and generate a pathology index for qualitative cardiac arrhythmia characterization. The known systems fail to provide adequate information on tissue mapping and arrhythmia localization and are subjective and burdensome to use for clinical data interpretation and proper cardiac rhythm management.
Known systems typically analyze time characteristics (amplitude, latency) or frequency domain (power, spectrum) changes but these often fail to accurately capture and characterize small signal changes in a portion (P wave, QRS complex, ST segment) of a heart beat cycle. Consequently, known systems may fail to detect arrhythmia or initiate a false alarm (for example, or indicate a FN (false negative)). A percentage of false negative results represents patients who do have disease X, but for whom a screening test wrongly indicates they do not have disease X. Also known systems relying on amplitude (voltage) change detection may be inaccurate for cardiac function evaluation and pathology diagnosis. Time domain and frequency domain parameter based analysis fails to provide comprehensive detailed indication of severity of pathology, location of abnormal tissue (such as muscle, chamber) and fail to associate signal frequency variation with cardiac pathological functional changes and may not adequately capture a signal portion (such as a region of interest (ROI) in cardiac electrophysiological signals). Known systems are typically unable to quantitatively capture and characterize changes, and predict a pathological trend such as a pathology trend from low risk to medium, and then to high risk (severe and fatal) rhythm (especially a VT growing arrhythmia). Further, noise and artifact sensitivity and stability impairs arrhythmia detection of known cardiac function monitoring systems. A system according to invention principles addresses these deficiencies and related problems.